[Defense] Image Quality for Object Detection in Compressed Videos
Friday, July 15, 2022
11:00 am - 12:00 pm
will defend her dissertation
Image Quality for Object Detection in Compressed Videos
The amount of video data generated daily is enormous, making it nearly impossible for humans to understand the content of the data. The use of machine learning and deep learning approaches for automatic analysis has grown. It is crucial to determine the robustness and reliability of the automated analysis. The reliability of automated systems can be evaluated using a variety of factors. One such parameter is compression, which is an inherent part in video transmission and storage. We analyzed the impact of compression on three computer vision algorithms. The dataset used for analysis is collected from an IP-based surveillance camera and compressed using different bandwidths and quantization levels. We also find a correlation between image quality and the performance of algorithms. Existing image quality metrics cannot explain the drop in performance for object detection. The traditional image quality metrics define quality from a human perspective. We introduced full-reference and no-reference metrics to overcome the constraints of existing image quality metrics. We present the performance of the image quality metric on different aspects of object detection.
11:00AM - 12:00PM CT
Online via MS Teams
Dr. Shishir Shah, dissertation advisor
Faculty, students and the general public are invited.